3-N-butylphthalide is an ettectwe drug for acute iscemlc stroke. However, its effects on cnromc cerebral ischemia-induced neuronal injury remain poorly understood. Therefore, this study li- gated bilateral carotid art...3-N-butylphthalide is an ettectwe drug for acute iscemlc stroke. However, its effects on cnromc cerebral ischemia-induced neuronal injury remain poorly understood. Therefore, this study li- gated bilateral carotid arteries in 15-month-old rats to simulate chronic cerebral ischemia in aged humans. Aged rats were then intragastrically administered 3-n-butylphthalide. 3-N-butylphtha- lide administration improved the neuronal morphology in the cerebral cortex and hippocampus of rats with chronic cerebral ischemia, increased choline acetyltransferase activity, and decreased malondialdehyde and amyloid beta levels, and greatly improved cognitive function. These findings suggest that 3-n-butylphthalide alleviates oxidative stress caused by chronic cerebral ischemia, improves cholinergic function, and inhibits amyloid beta accumulation, thereby im- proving cerebral neuronal injury and cognitive deficits.展开更多
Classifying large-scale networks into several categories and distinguishing them according to their fine structures is of great importance to several real-life applications.However,most studies on complex networks foc...Classifying large-scale networks into several categories and distinguishing them according to their fine structures is of great importance to several real-life applications.However,most studies on complex networks focus on the properties of a single network and seldom on classification,clustering,and comparison between different networks,in which the network is treated as a whole.Conventional methods can hardly be applied on networks directly due to the non-Euclidean properties of data.In this paper,we propose a novel framework of Complex Network Classifier(CNC)by integrating network embedding and convolutional neural network to tackle the problem of network classification.By training the classifier on synthetic complex network data,we show CNC can not only classify networks with high accuracy and robustness but can also extract the features of the networks automatically.We also compare our CNC with baseline methods on benchmark datasets,which shows that our method performs well on large-scale networks.展开更多
Depression is closely linked to the morphology and functional abnormalities of multiple brain regions; however, its topological structure throughout the whole brain remains unclear. We col- lected resting-state functi...Depression is closely linked to the morphology and functional abnormalities of multiple brain regions; however, its topological structure throughout the whole brain remains unclear. We col- lected resting-state functional MRI data from 36 first-onset unmedicated depression patients and 27 healthy controls. The resting-state functional connectivity was constructed using the Auto- mated Anatomical Labeling template with a partial correlation method. The metrics calculation and statistical analysis were performed using complex network theory. The results showed that both depressive patients and healthy controls presented typical small-world attributes. Compared with healthy controls, characteristic path length was significantly shorter in depressive patients, suggesting development toward randomization. Patients with depression showed apparently abnormal node attributes at key areas in cortical-striatal-pallidal-thalamic circuits. In addition, right hippocampus and right thalamus were closely linked with the severity of depression. We se- lected 270 local attributes as the classification features and their P values were regarded as criteria for statistically significant differences. An artificial neural network algorithm was applied for classification research. The results showed that brain network metrics could be used as an effec- tive feature in machine learning research, which brings about a reasonable application prospect for brain network metrics. The present study also highlighted a significant positive correlation between the importance of the attributes and the intergroup differences; that is, the more sig- nificant the differences in node attributes, the stronger their contribution to the classification. Experimental findings indicate that statistical significance is an effective quantitative indicator of the selection of brain network metrics and can assist the clinical diagnosis of depression.展开更多
An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique...An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the Laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.展开更多
Fructose-1,6-diphosphate is a metabolic intermediate that promotes cell metabolism. We hypothesize that fructose-1,6-diphosphate can protect against neuronal damage induced by febrile convulsions. Hot-water bathing wa...Fructose-1,6-diphosphate is a metabolic intermediate that promotes cell metabolism. We hypothesize that fructose-1,6-diphosphate can protect against neuronal damage induced by febrile convulsions. Hot-water bathing was used to establish a repetitive febrile convulsion model in rats aged 21 days, equivalent to 3–5 years in humans. Ninety minutes before each seizure induction, rats received an intraperitoneal injection of low- or high-dose fructose-1,6-diphosphate（500 or 1,000 mg/kg, respectively）. Low- and high-dose fructose-1,6-diphosphate prolonged the latency and shortened the duration of seizures. Furthermore, high-dose fructose-1,6-diphosphate effectively reduced seizure severity. Transmission electron microscopy revealed that 24 hours after the last seizure, high-dose fructose-1,6-diphosphate reduced mitochondrial swelling, rough endoplasmic reticulum degranulation, Golgi dilation and synaptic cleft size, and increased synaptic active zone length, postsynaptic density thickness, and synaptic interface curvature in the hippocampal CA1 area. The present findings suggest that fructose-1,6-diphosphate is a neuroprotectant against hippocampal neuron and synapse damage induced by repeated febrile convulsion in immature rats.展开更多
The prediction of precipitation at subseasonal to seasonal(S2S)timescales remains an enormous challenge because of the gap between weather and climate predictions.This study compares three deep learning algorithms,nam...The prediction of precipitation at subseasonal to seasonal(S2S)timescales remains an enormous challenge because of the gap between weather and climate predictions.This study compares three deep learning algorithms,namely,the long short-term memory recurrent(LSTM),gated recurrent unit(GRU),and recurrent neural network(RNN),and selects the optimal algorithm to establish an S2S precipitation prediction model.The models were evaluated in four subregions of the Sichuan Province:the Plateau,Valley,eastern Basin,and western Basin.The results showed that the RNN model had better performance than the LSTM and GRU models.This could be because the RNN model had an advantage over the LSTM model in the transformation of climate indices with positive and negative variations.In the validation of test datasets,the RNN model successfully predicted the precipitation trend in most years during the wet season(May-October).The RNN model had a lower prediction bias(within±10%),higher sign accuracy of the precipitation trend(~88.95%),and greater accuracy of the maximum precipitation month(>0.85).For the prediction of different lead times,the RNN model was able to provide a stable trend prediction for summer precipitation,and the time correlation coefficient score was higher than that of the National Climate Center of China.Furthermore,this study proposed a method to measure the sensitivity of the RNN model to different input features,which may provide unprecedented insights into the nonlinear relationship and complicated feedback process among climate systems.The results of the sensitivity distribution are as follows.First,the Niño 4 and Niño 3.4 indices were equally important for the prediction of wet season precipitation.Second,the sensitivity of the snow cover on the Tibetan Plateau was higher than that in the Northern Hemisphere.Third,an opposite sensitivity appeared in two different patterns of the Indian Ocean and sea ice concentrations in the Arctic and the Barents Sea.展开更多
Different fates of neural stem/progenitor cells(NSPCs)and their progeny are determined by the gene regulatory network,where a chromatin-remodeling complex affects synergy with other regulators.Here,we review recent re...Different fates of neural stem/progenitor cells(NSPCs)and their progeny are determined by the gene regulatory network,where a chromatin-remodeling complex affects synergy with other regulators.Here,we review recent research progress indicating that the BRG1/BRM-associated factor(BAF)complex plays an important role in NSPCs during neural development and neural developmental disorders.Several studies based on animal models have shown that mutations in the BAF complex may cause abnormal neural differentiation,which can also lead to various diseases in humans.We discussed BAF complex subunits and their main characteristics in NSPCs.With advances in studies of human pluripotent stem cells and the feasibility of driving their differentiation into NSPCs,we can now investigate the role of the BAF complex in regulating the balance between self-renewal and differentiation of NSPCs.Considering recent progress in these research areas,we suggest that three approaches should be used in investigations in the near future.Sequencing of whole human exome and genome-wide association studies suggest that mutations in the subunits of the BAF complex are related to neurodevelopmental disorders.More insight into the mechanism of BAF complex regulation in NSPCs during neural cell fate decisions and neurodevelopment may help in exploiting new methods for clinical applications.展开更多
IT has been demonstrated that two reproductive hormones,gonadotropin-releasing hormone(GnRH)and gonadotropin(GTH),exist in the nervous system and Hatschek’s pit oflancelet,a species of Cephalochordata,and that these ...IT has been demonstrated that two reproductive hormones,gonadotropin-releasing hormone(GnRH)and gonadotropin(GTH),exist in the nervous system and Hatschek’s pit oflancelet,a species of Cephalochordata,and that these hormones are involved in the regulationof gonadal development and reproductive activity of lancelet.However,no report could展开更多
基金supported by Innovation Team Project of Hubei Province 2011 Plans,No.2011JH-2013CXTT06Momentous Scientific Research Funds of Hubei Provincial Education Ministry,No.D20102101Cultivating Funds of Country’s Projects of Hubei University of Medicine,No.2013GPY03
文摘3-N-butylphthalide is an ettectwe drug for acute iscemlc stroke. However, its effects on cnromc cerebral ischemia-induced neuronal injury remain poorly understood. Therefore, this study li- gated bilateral carotid arteries in 15-month-old rats to simulate chronic cerebral ischemia in aged humans. Aged rats were then intragastrically administered 3-n-butylphthalide. 3-N-butylphtha- lide administration improved the neuronal morphology in the cerebral cortex and hippocampus of rats with chronic cerebral ischemia, increased choline acetyltransferase activity, and decreased malondialdehyde and amyloid beta levels, and greatly improved cognitive function. These findings suggest that 3-n-butylphthalide alleviates oxidative stress caused by chronic cerebral ischemia, improves cholinergic function, and inhibits amyloid beta accumulation, thereby im- proving cerebral neuronal injury and cognitive deficits.
基金supported by the National Natural Science Foundation of China(No.61673070)Beijing Normal University Interdisciplinary Project.
文摘Classifying large-scale networks into several categories and distinguishing them according to their fine structures is of great importance to several real-life applications.However,most studies on complex networks focus on the properties of a single network and seldom on classification,clustering,and comparison between different networks,in which the network is treated as a whole.Conventional methods can hardly be applied on networks directly due to the non-Euclidean properties of data.In this paper,we propose a novel framework of Complex Network Classifier(CNC)by integrating network embedding and convolutional neural network to tackle the problem of network classification.By training the classifier on synthetic complex network data,we show CNC can not only classify networks with high accuracy and robustness but can also extract the features of the networks automatically.We also compare our CNC with baseline methods on benchmark datasets,which shows that our method performs well on large-scale networks.
基金supported by the National Natural Science Foundation of China,No.61070077,61170136,61373101,81171290the Natural Science Foundation of Shanxi Province in China,No.2010011020-2,2011011015-4+3 种基金Programs for Science and Technology Social Development of Shanxi Province,No.20130313012-2Science and Technology Projects by Shanxi Provincial Ed-ucation Ministry,No.20121003Youth Fund by Taiyuan University of Technology,No.2012L014Youth Team Fund by Taiyuan University of Technology,No.2013T047
文摘Depression is closely linked to the morphology and functional abnormalities of multiple brain regions; however, its topological structure throughout the whole brain remains unclear. We col- lected resting-state functional MRI data from 36 first-onset unmedicated depression patients and 27 healthy controls. The resting-state functional connectivity was constructed using the Auto- mated Anatomical Labeling template with a partial correlation method. The metrics calculation and statistical analysis were performed using complex network theory. The results showed that both depressive patients and healthy controls presented typical small-world attributes. Compared with healthy controls, characteristic path length was significantly shorter in depressive patients, suggesting development toward randomization. Patients with depression showed apparently abnormal node attributes at key areas in cortical-striatal-pallidal-thalamic circuits. In addition, right hippocampus and right thalamus were closely linked with the severity of depression. We se- lected 270 local attributes as the classification features and their P values were regarded as criteria for statistically significant differences. An artificial neural network algorithm was applied for classification research. The results showed that brain network metrics could be used as an effec- tive feature in machine learning research, which brings about a reasonable application prospect for brain network metrics. The present study also highlighted a significant positive correlation between the importance of the attributes and the intergroup differences; that is, the more sig- nificant the differences in node attributes, the stronger their contribution to the classification. Experimental findings indicate that statistical significance is an effective quantitative indicator of the selection of brain network metrics and can assist the clinical diagnosis of depression.
基金Acknowledgments The research work presented in this paper was partialy supported by the National Natural Science Foundation of China （Grant No. 61173015 ＆ 61573257）.
文摘An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the Laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.
基金financially supported by the Medical Innovations Fund of Xi’an Jiaotong University,No.GH0203214Shaanxi Provincial People’s Hospital Incubator Fund Projects+1 种基金the National Natural Science Foundation of China,No.30901600Shaanxi Provincial Scientific and Technological Research Projects,No.2006K14-G12,2005K14-G7
文摘Fructose-1,6-diphosphate is a metabolic intermediate that promotes cell metabolism. We hypothesize that fructose-1,6-diphosphate can protect against neuronal damage induced by febrile convulsions. Hot-water bathing was used to establish a repetitive febrile convulsion model in rats aged 21 days, equivalent to 3–5 years in humans. Ninety minutes before each seizure induction, rats received an intraperitoneal injection of low- or high-dose fructose-1,6-diphosphate（500 or 1,000 mg/kg, respectively）. Low- and high-dose fructose-1,6-diphosphate prolonged the latency and shortened the duration of seizures. Furthermore, high-dose fructose-1,6-diphosphate effectively reduced seizure severity. Transmission electron microscopy revealed that 24 hours after the last seizure, high-dose fructose-1,6-diphosphate reduced mitochondrial swelling, rough endoplasmic reticulum degranulation, Golgi dilation and synaptic cleft size, and increased synaptic active zone length, postsynaptic density thickness, and synaptic interface curvature in the hippocampal CA1 area. The present findings suggest that fructose-1,6-diphosphate is a neuroprotectant against hippocampal neuron and synapse damage induced by repeated febrile convulsion in immature rats.
基金the National Natural Science Foundation of China(Nos.U20A2097,42175042)the Natural Science Foundation of Sichuan(Nos.2022NSFSC1056,2023NSFSC0246)+3 种基金the China Scholarship Council(No.201908510031)the Plateau and Basin Rainstorm,Drought and Flood Key Laboratory of Sichuan Province(Nos.SCQXKJZD202102-6,SCQXKJYJXMS202102)the Innovation Team Fund of Southwest Regional Meteorological Center,China Meteorological Administration(No.XNQYCXTD202201)the Sichuan Science and Technology Program(No.2022YFS0544).
文摘The prediction of precipitation at subseasonal to seasonal(S2S)timescales remains an enormous challenge because of the gap between weather and climate predictions.This study compares three deep learning algorithms,namely,the long short-term memory recurrent(LSTM),gated recurrent unit(GRU),and recurrent neural network(RNN),and selects the optimal algorithm to establish an S2S precipitation prediction model.The models were evaluated in four subregions of the Sichuan Province:the Plateau,Valley,eastern Basin,and western Basin.The results showed that the RNN model had better performance than the LSTM and GRU models.This could be because the RNN model had an advantage over the LSTM model in the transformation of climate indices with positive and negative variations.In the validation of test datasets,the RNN model successfully predicted the precipitation trend in most years during the wet season(May-October).The RNN model had a lower prediction bias(within±10%),higher sign accuracy of the precipitation trend(~88.95%),and greater accuracy of the maximum precipitation month(>0.85).For the prediction of different lead times,the RNN model was able to provide a stable trend prediction for summer precipitation,and the time correlation coefficient score was higher than that of the National Climate Center of China.Furthermore,this study proposed a method to measure the sensitivity of the RNN model to different input features,which may provide unprecedented insights into the nonlinear relationship and complicated feedback process among climate systems.The results of the sensitivity distribution are as follows.First,the Niño 4 and Niño 3.4 indices were equally important for the prediction of wet season precipitation.Second,the sensitivity of the snow cover on the Tibetan Plateau was higher than that in the Northern Hemisphere.Third,an opposite sensitivity appeared in two different patterns of the Indian Ocean and sea ice concentrations in the Arctic and the Barents Sea.
基金Supported by the Natural Science Foundation of Anhui Province,No.2008085MH251Key Research and Development Project of Anhui Province,No.202004J07020037+1 种基金Anhui Provincial Institute of Translational Medicine,No.2021zhyx-C19National Undergraduate Innovation and Entrepreneurship training program,No.202010366016。
文摘Different fates of neural stem/progenitor cells(NSPCs)and their progeny are determined by the gene regulatory network,where a chromatin-remodeling complex affects synergy with other regulators.Here,we review recent research progress indicating that the BRG1/BRM-associated factor(BAF)complex plays an important role in NSPCs during neural development and neural developmental disorders.Several studies based on animal models have shown that mutations in the BAF complex may cause abnormal neural differentiation,which can also lead to various diseases in humans.We discussed BAF complex subunits and their main characteristics in NSPCs.With advances in studies of human pluripotent stem cells and the feasibility of driving their differentiation into NSPCs,we can now investigate the role of the BAF complex in regulating the balance between self-renewal and differentiation of NSPCs.Considering recent progress in these research areas,we suggest that three approaches should be used in investigations in the near future.Sequencing of whole human exome and genome-wide association studies suggest that mutations in the subunits of the BAF complex are related to neurodevelopmental disorders.More insight into the mechanism of BAF complex regulation in NSPCs during neural cell fate decisions and neurodevelopment may help in exploiting new methods for clinical applications.
文摘IT has been demonstrated that two reproductive hormones,gonadotropin-releasing hormone(GnRH)and gonadotropin(GTH),exist in the nervous system and Hatschek’s pit oflancelet,a species of Cephalochordata,and that these hormones are involved in the regulationof gonadal development and reproductive activity of lancelet.However,no report could
